Highly Flexible Deep-Learning-Based Automatic Analysis for Graphically Encoded Hydrogel Microparticles

Abstract

Graphically encoded hydrogel microparticle (HMP)-based bioassay is a diagnostic tool characterized by exceptional multiplex detectability and robust sensitivity and specificity. Specifically, deep learning enables highly fast and accurate analyses of HMPs with diverse graphical codes. However, previous related studies have found the use of plain particles as data to be disadvantageous for accurate analyses of HMPs loaded with functional nanomaterials. Furthermore, the manual data annotation method used in existing approaches is highly labor-intensive and time-consuming. In this study, we present an efficient deep-learning-based analysis of encoded HMPs with diverse graphical codes and functional nanomaterials, utilizing the auto-annotation and synthetic data mixing methods for model training. The auto-annotation enhanced the throughput of dataset preparation up to 0.11 s/image. Using synthetic data mixing, a mean average precision of 0.88 was achieved in the analysis of encoded HMPs with magnetic nanoparticles, representing an approximately twofold improvement over the standard method. To evaluate the practical applicability of the proposed automatic analysis strategy, a single-image analysis was performed after the triplex immunoassay for the preeclampsia-related protein biomarkers. Finally, we accomplished a processing throughput of 0.353 s per sample for analyzing the result image.